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infosys1-4.ipynb

  1. Milestone1
  2. importing netflix movie and tv shows dataset from kaggle
  3. Milestone2
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Python [conda env:base] *
Kernel status: Idle
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    # Milestone1

    Milestone1¶

    # importing netflix movie and tv shows dataset from kaggle
    import pandas as pd
    df=pd.read_csv(r"C:\Users\saiga\Downloads\netflix_titles.csv\netflix_titles.csv")
    df.head()
    df.info()
    [146]:
    Selection deleted
    #checking missing values
    df.isnull().sum()
    [146]:
    show_id            0
    type               0
    title              0
    director        2634
    cast             825
    country          831
    date_added        10
    release_year       0
    rating             4
    duration           3
    listed_in          0
    description        0
    dtype: int64
    [174]:
    # Handling Missing values
    df['country'].fillna('Unknown', inplace=True)
    df['rating'].fillna(df['rating'].mode()[0], inplace=True)
    df['director'].fillna('Unknown',inplace=True)
    df['cast'].fillna('Unknown',inplace=True)
    df['duration'].fillna(df['duration'].mode()[0], inplace=True)
    df['date_added'].fillna('not specified',inplace=True)
    df.isnull().sum()
    C:\Users\saiga\AppData\Local\Temp\ipykernel_26280\2888745227.py:2: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
    The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
    
    For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
    
    
      df['country'].fillna('Unknown', inplace=True)
    C:\Users\saiga\AppData\Local\Temp\ipykernel_26280\2888745227.py:3: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
    The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
    
    For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
    
    
      df['rating'].fillna(df['rating'].mode()[0], inplace=True)
    C:\Users\saiga\AppData\Local\Temp\ipykernel_26280\2888745227.py:4: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
    The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
    
    For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
    
    
      df['director'].fillna('Unknown',inplace=True)
    C:\Users\saiga\AppData\Local\Temp\ipykernel_26280\2888745227.py:5: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
    The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
    
    For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
    
    
      df['cast'].fillna('Unknown',inplace=True)
    C:\Users\saiga\AppData\Local\Temp\ipykernel_26280\2888745227.py:6: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
    The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
    
    For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
    
    
      df['duration'].fillna(df['duration'].mode()[0], inplace=True)
    C:\Users\saiga\AppData\Local\Temp\ipykernel_26280\2888745227.py:7: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
    The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
    
    For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
    
    
      df['date_added'].fillna('not specified',inplace=True)
    
    [174]:
    show_id         0
    type            0
    title           0
    director        0
    cast            0
    country         0
    date_added      0
    release_year    0
    rating          0
    duration        0
    listed_in       0
    description     0
    dtype: int64
    [170]:
    0
    [238]:
    <Axes: xlabel='type'>
    [260]:
    <Axes: ylabel='count'>
    [270]:
    # visaulize the distribution of rating
    df['rating'].value_counts().plot(kind='line')
    [270]:
    <Axes: xlabel='rating'>
    [302]:
    country
    United States     3211
    India             1008
    Unknown            831
    United Kingdom     628
    Canada             271
    Japan              259
    France             212
    South Korea        211
    Spain              181
    Mexico             134
    Name: count, dtype: int64
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